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<li><a class="reference internal" href="#"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.ensemble</span></code>.IsolationForest</a><ul>
<li><a class="reference internal" href="#examples-using-sklearn-ensemble-isolationforest">Examples using <code class="docutils literal notranslate"><span class="pre">sklearn.ensemble.IsolationForest</span></code></a></li>
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  <div class="section" id="sklearn-ensemble-isolationforest">
<h1><a class="reference internal" href="../classes.html#module-sklearn.ensemble" title="sklearn.ensemble"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.ensemble</span></code></a>.IsolationForest<a class="headerlink" href="#sklearn-ensemble-isolationforest" title="Permalink to this headline">¶</a></h1>
<dl class="class">
<dt id="sklearn.ensemble.IsolationForest">
<em class="property">class </em><code class="sig-prename descclassname">sklearn.ensemble.</code><code class="sig-name descname">IsolationForest</code><span class="sig-paren">(</span><em class="sig-param">n_estimators=100</em>, <em class="sig-param">max_samples='auto'</em>, <em class="sig-param">contamination='auto'</em>, <em class="sig-param">max_features=1.0</em>, <em class="sig-param">bootstrap=False</em>, <em class="sig-param">n_jobs=None</em>, <em class="sig-param">behaviour='deprecated'</em>, <em class="sig-param">random_state=None</em>, <em class="sig-param">verbose=0</em>, <em class="sig-param">warm_start=False</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/ensemble/_iforest.py#L26"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.ensemble.IsolationForest" title="Permalink to this definition">¶</a></dt>
<dd><p>Isolation Forest Algorithm.</p>
<p>Return the anomaly score of each sample using the IsolationForest algorithm</p>
<p>The IsolationForest ‘isolates’ observations by randomly selecting a feature
and then randomly selecting a split value between the maximum and minimum
values of the selected feature.</p>
<p>Since recursive partitioning can be represented by a tree structure, the
number of splittings required to isolate a sample is equivalent to the path
length from the root node to the terminating node.</p>
<p>This path length, averaged over a forest of such random trees, is a
measure of normality and our decision function.</p>
<p>Random partitioning produces noticeably shorter paths for anomalies.
Hence, when a forest of random trees collectively produce shorter path
lengths for particular samples, they are highly likely to be anomalies.</p>
<p>Read more in the <a class="reference internal" href="../outlier_detection.html#isolation-forest"><span class="std std-ref">User Guide</span></a>.</p>
<div class="versionadded">
<p><span class="versionmodified added">New in version 0.18.</span></p>
</div>
<dl class="field-list">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl>
<dt><strong>n_estimators</strong><span class="classifier">int, optional (default=100)</span></dt><dd><p>The number of base estimators in the ensemble.</p>
</dd>
<dt><strong>max_samples</strong><span class="classifier">int or float, optional (default=”auto”)</span></dt><dd><dl class="simple">
<dt>The number of samples to draw from X to train each base estimator.</dt><dd><ul class="simple">
<li><p>If int, then draw <code class="docutils literal notranslate"><span class="pre">max_samples</span></code> samples.</p></li>
<li><p>If float, then draw <code class="docutils literal notranslate"><span class="pre">max_samples</span> <span class="pre">*</span> <span class="pre">X.shape[0]</span></code> samples.</p></li>
<li><p>If “auto”, then <code class="docutils literal notranslate"><span class="pre">max_samples=min(256,</span> <span class="pre">n_samples)</span></code>.</p></li>
</ul>
</dd>
</dl>
<p>If max_samples is larger than the number of samples provided,
all samples will be used for all trees (no sampling).</p>
</dd>
<dt><strong>contamination</strong><span class="classifier">‘auto’ or float, optional (default=’auto’)</span></dt><dd><p>The amount of contamination of the data set, i.e. the proportion
of outliers in the data set. Used when fitting to define the threshold
on the scores of the samples.</p>
<blockquote>
<div><ul class="simple">
<li><p>If ‘auto’, the threshold is determined as in the
original paper.</p></li>
<li><p>If float, the contamination should be in the range [0, 0.5].</p></li>
</ul>
</div></blockquote>
<div class="versionchanged">
<p><span class="versionmodified changed">Changed in version 0.22: </span>The default value of <code class="docutils literal notranslate"><span class="pre">contamination</span></code> changed from 0.1
to <code class="docutils literal notranslate"><span class="pre">'auto'</span></code>.</p>
</div>
</dd>
<dt><strong>max_features</strong><span class="classifier">int or float, optional (default=1.0)</span></dt><dd><p>The number of features to draw from X to train each base estimator.</p>
<blockquote>
<div><ul class="simple">
<li><p>If int, then draw <code class="docutils literal notranslate"><span class="pre">max_features</span></code> features.</p></li>
<li><p>If float, then draw <code class="docutils literal notranslate"><span class="pre">max_features</span> <span class="pre">*</span> <span class="pre">X.shape[1]</span></code> features.</p></li>
</ul>
</div></blockquote>
</dd>
<dt><strong>bootstrap</strong><span class="classifier">bool, optional (default=False)</span></dt><dd><p>If True, individual trees are fit on random subsets of the training
data sampled with replacement. If False, sampling without replacement
is performed.</p>
</dd>
<dt><strong>n_jobs</strong><span class="classifier">int or None, optional (default=None)</span></dt><dd><p>The number of jobs to run in parallel for both <a class="reference internal" href="#sklearn.ensemble.IsolationForest.fit" title="sklearn.ensemble.IsolationForest.fit"><code class="xref py py-meth docutils literal notranslate"><span class="pre">fit</span></code></a> and
<a class="reference internal" href="#sklearn.ensemble.IsolationForest.predict" title="sklearn.ensemble.IsolationForest.predict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">predict</span></code></a>. <code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a
<a class="reference external" href="https://joblib.readthedocs.io/en/latest/parallel.html#joblib.parallel_backend" title="(in joblib v0.14.1.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context. <code class="docutils literal notranslate"><span class="pre">-1</span></code> means using all
processors. See <a class="reference internal" href="../../glossary.html#term-n-jobs"><span class="xref std std-term">Glossary</span></a> for more details.</p>
</dd>
<dt><strong>behaviour</strong><span class="classifier">str, default=’deprecated’</span></dt><dd><p>This parameter has not effect, is deprecated, and will be removed.</p>
<div class="versionadded">
<p><span class="versionmodified added">New in version 0.20: </span><code class="docutils literal notranslate"><span class="pre">behaviour</span></code> is added in 0.20 for back-compatibility purpose.</p>
</div>
<div class="deprecated">
<p><span class="versionmodified deprecated">Deprecated since version 0.20: </span><code class="docutils literal notranslate"><span class="pre">behaviour='old'</span></code> is deprecated in 0.20 and will not be possible
in 0.22.</p>
</div>
<div class="deprecated">
<p><span class="versionmodified deprecated">Deprecated since version 0.22: </span><code class="docutils literal notranslate"><span class="pre">behaviour</span></code> parameter is deprecated in 0.22 and removed in
0.24.</p>
</div>
</dd>
<dt><strong>random_state</strong><span class="classifier">int, RandomState instance or None, optional (default=None)</span></dt><dd><p>If int, random_state is the seed used by the random number generator;
If RandomState instance, random_state is the random number generator;
If None, the random number generator is the RandomState instance used
by <code class="docutils literal notranslate"><span class="pre">np.random</span></code>.</p>
</dd>
<dt><strong>verbose</strong><span class="classifier">int, optional (default=0)</span></dt><dd><p>Controls the verbosity of the tree building process.</p>
</dd>
<dt><strong>warm_start</strong><span class="classifier">bool, optional (default=False)</span></dt><dd><p>When set to <code class="docutils literal notranslate"><span class="pre">True</span></code>, reuse the solution of the previous call to fit
and add more estimators to the ensemble, otherwise, just fit a whole
new forest. See <a class="reference internal" href="../../glossary.html#term-warm-start"><span class="xref std std-term">the Glossary</span></a>.</p>
<div class="versionadded">
<p><span class="versionmodified added">New in version 0.21.</span></p>
</div>
</dd>
</dl>
</dd>
<dt class="field-even">Attributes</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>estimators_</strong><span class="classifier">list of DecisionTreeClassifier</span></dt><dd><p>The collection of fitted sub-estimators.</p>
</dd>
<dt><a class="reference internal" href="#sklearn.ensemble.IsolationForest.estimators_samples_" title="sklearn.ensemble.IsolationForest.estimators_samples_"><code class="xref py py-obj docutils literal notranslate"><span class="pre">estimators_samples_</span></code></a><span class="classifier">list of arrays</span></dt><dd><p>The subset of drawn samples for each base estimator.</p>
</dd>
<dt><strong>max_samples_</strong><span class="classifier">integer</span></dt><dd><p>The actual number of samples</p>
</dd>
<dt><strong>offset_</strong><span class="classifier">float</span></dt><dd><p>Offset used to define the decision function from the raw scores. We
have the relation: <code class="docutils literal notranslate"><span class="pre">decision_function</span> <span class="pre">=</span> <span class="pre">score_samples</span> <span class="pre">-</span> <span class="pre">offset_</span></code>.
<code class="docutils literal notranslate"><span class="pre">offset_</span></code> is defined as follows. When the contamination parameter is
set to “auto”, the offset is equal to -0.5 as the scores of inliers are
close to 0 and the scores of outliers are close to -1. When a
contamination parameter different than “auto” is provided, the offset
is defined in such a way we obtain the expected number of outliers
(samples with decision function &lt; 0) in training.</p>
</dd>
</dl>
</dd>
</dl>
<div class="admonition seealso">
<p class="admonition-title">See also</p>
<dl class="simple">
<dt><a class="reference internal" href="sklearn.covariance.EllipticEnvelope.html#sklearn.covariance.EllipticEnvelope" title="sklearn.covariance.EllipticEnvelope"><code class="xref py py-obj docutils literal notranslate"><span class="pre">sklearn.covariance.EllipticEnvelope</span></code></a></dt><dd><p>An object for detecting outliers in a Gaussian distributed dataset.</p>
</dd>
<dt><a class="reference internal" href="sklearn.svm.OneClassSVM.html#sklearn.svm.OneClassSVM" title="sklearn.svm.OneClassSVM"><code class="xref py py-obj docutils literal notranslate"><span class="pre">sklearn.svm.OneClassSVM</span></code></a></dt><dd><p>Unsupervised Outlier Detection. Estimate the support of a high-dimensional distribution. The implementation is based on libsvm.</p>
</dd>
<dt><a class="reference internal" href="sklearn.neighbors.LocalOutlierFactor.html#sklearn.neighbors.LocalOutlierFactor" title="sklearn.neighbors.LocalOutlierFactor"><code class="xref py py-obj docutils literal notranslate"><span class="pre">sklearn.neighbors.LocalOutlierFactor</span></code></a></dt><dd><p>Unsupervised Outlier Detection using Local Outlier Factor (LOF).</p>
</dd>
</dl>
</div>
<p class="rubric">Notes</p>
<p>The implementation is based on an ensemble of ExtraTreeRegressor. The
maximum depth of each tree is set to <code class="docutils literal notranslate"><span class="pre">ceil(log_2(n))</span></code> where
<span class="math notranslate nohighlight">\(n\)</span> is the number of samples used to build the tree
(see (Liu et al., 2008) for more details).</p>
<p class="rubric">References</p>
<dl class="citation">
<dt class="label" id="rd7ae0a2ae688-1"><span class="brackets">Rd7ae0a2ae688-1</span></dt>
<dd><p>Liu, Fei Tony, Ting, Kai Ming and Zhou, Zhi-Hua. “Isolation forest.”
Data Mining, 2008. ICDM’08. Eighth IEEE International Conference on.</p>
</dd>
<dt class="label" id="rd7ae0a2ae688-2"><span class="brackets">Rd7ae0a2ae688-2</span></dt>
<dd><p>Liu, Fei Tony, Ting, Kai Ming and Zhou, Zhi-Hua. “Isolation-based
anomaly detection.” ACM Transactions on Knowledge Discovery from
Data (TKDD) 6.1 (2012): 3.</p>
</dd>
</dl>
<p class="rubric">Examples</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">sklearn.ensemble</span> <span class="kn">import</span> <span class="n">IsolationForest</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">X</span> <span class="o">=</span> <span class="p">[[</span><span class="o">-</span><span class="mf">1.1</span><span class="p">],</span> <span class="p">[</span><span class="mf">0.3</span><span class="p">],</span> <span class="p">[</span><span class="mf">0.5</span><span class="p">],</span> <span class="p">[</span><span class="mi">100</span><span class="p">]]</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">clf</span> <span class="o">=</span> <span class="n">IsolationForest</span><span class="p">(</span><span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">clf</span><span class="o">.</span><span class="n">predict</span><span class="p">([[</span><span class="mf">0.1</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">90</span><span class="p">]])</span>
<span class="go">array([ 1,  1, -1])</span>
</pre></div>
</div>
<p class="rubric">Methods</p>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="#sklearn.ensemble.IsolationForest.decision_function" title="sklearn.ensemble.IsolationForest.decision_function"><code class="xref py py-obj docutils literal notranslate"><span class="pre">decision_function</span></code></a>(self, X)</p></td>
<td><p>Average anomaly score of X of the base classifiers.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#sklearn.ensemble.IsolationForest.fit" title="sklearn.ensemble.IsolationForest.fit"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fit</span></code></a>(self, X[, y, sample_weight])</p></td>
<td><p>Fit estimator.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#sklearn.ensemble.IsolationForest.fit_predict" title="sklearn.ensemble.IsolationForest.fit_predict"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fit_predict</span></code></a>(self, X[, y])</p></td>
<td><p>Perform fit on X and returns labels for X.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#sklearn.ensemble.IsolationForest.get_params" title="sklearn.ensemble.IsolationForest.get_params"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_params</span></code></a>(self[, deep])</p></td>
<td><p>Get parameters for this estimator.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#sklearn.ensemble.IsolationForest.predict" title="sklearn.ensemble.IsolationForest.predict"><code class="xref py py-obj docutils literal notranslate"><span class="pre">predict</span></code></a>(self, X)</p></td>
<td><p>Predict if a particular sample is an outlier or not.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#sklearn.ensemble.IsolationForest.score_samples" title="sklearn.ensemble.IsolationForest.score_samples"><code class="xref py py-obj docutils literal notranslate"><span class="pre">score_samples</span></code></a>(self, X)</p></td>
<td><p>Opposite of the anomaly score defined in the original paper.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#sklearn.ensemble.IsolationForest.set_params" title="sklearn.ensemble.IsolationForest.set_params"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_params</span></code></a>(self, \*\*params)</p></td>
<td><p>Set the parameters of this estimator.</p></td>
</tr>
</tbody>
</table>
<dl class="method">
<dt id="sklearn.ensemble.IsolationForest.__init__">
<code class="sig-name descname">__init__</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">n_estimators=100</em>, <em class="sig-param">max_samples='auto'</em>, <em class="sig-param">contamination='auto'</em>, <em class="sig-param">max_features=1.0</em>, <em class="sig-param">bootstrap=False</em>, <em class="sig-param">n_jobs=None</em>, <em class="sig-param">behaviour='deprecated'</em>, <em class="sig-param">random_state=None</em>, <em class="sig-param">verbose=0</em>, <em class="sig-param">warm_start=False</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/ensemble/_iforest.py#L181"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.ensemble.IsolationForest.__init__" title="Permalink to this definition">¶</a></dt>
<dd><p>Initialize self.  See help(type(self)) for accurate signature.</p>
</dd></dl>

<dl class="method">
<dt id="sklearn.ensemble.IsolationForest.decision_function">
<code class="sig-name descname">decision_function</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">X</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/ensemble/_iforest.py#L332"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.ensemble.IsolationForest.decision_function" title="Permalink to this definition">¶</a></dt>
<dd><p>Average anomaly score of X of the base classifiers.</p>
<p>The anomaly score of an input sample is computed as
the mean anomaly score of the trees in the forest.</p>
<p>The measure of normality of an observation given a tree is the depth
of the leaf containing this observation, which is equivalent to
the number of splittings required to isolate this point. In case of
several observations n_left in the leaf, the average path length of
a n_left samples isolation tree is added.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">array-like or sparse matrix, shape (n_samples, n_features)</span></dt><dd><p>The input samples. Internally, it will be converted to
<code class="docutils literal notranslate"><span class="pre">dtype=np.float32</span></code> and if a sparse matrix is provided
to a sparse <code class="docutils literal notranslate"><span class="pre">csr_matrix</span></code>.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>scores</strong><span class="classifier">array, shape (n_samples,)</span></dt><dd><p>The anomaly score of the input samples.
The lower, the more abnormal. Negative scores represent outliers,
positive scores represent inliers.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="method">
<dt id="sklearn.ensemble.IsolationForest.estimators_samples_">
<em class="property">property </em><code class="sig-name descname">estimators_samples_</code><a class="headerlink" href="#sklearn.ensemble.IsolationForest.estimators_samples_" title="Permalink to this definition">¶</a></dt>
<dd><p>The subset of drawn samples for each base estimator.</p>
<p>Returns a dynamically generated list of indices identifying
the samples used for fitting each member of the ensemble, i.e.,
the in-bag samples.</p>
<p>Note: the list is re-created at each call to the property in order
to reduce the object memory footprint by not storing the sampling
data. Thus fetching the property may be slower than expected.</p>
</dd></dl>

<dl class="method">
<dt id="sklearn.ensemble.IsolationForest.fit">
<code class="sig-name descname">fit</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">X</em>, <em class="sig-param">y=None</em>, <em class="sig-param">sample_weight=None</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/ensemble/_iforest.py#L221"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.ensemble.IsolationForest.fit" title="Permalink to this definition">¶</a></dt>
<dd><p>Fit estimator.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">array-like or sparse matrix, shape (n_samples, n_features)</span></dt><dd><p>The input samples. Use <code class="docutils literal notranslate"><span class="pre">dtype=np.float32</span></code> for maximum
efficiency. Sparse matrices are also supported, use sparse
<code class="docutils literal notranslate"><span class="pre">csc_matrix</span></code> for maximum efficiency.</p>
</dd>
<dt><strong>y</strong><span class="classifier">Ignored</span></dt><dd><p>Not used, present for API consistency by convention.</p>
</dd>
<dt><strong>sample_weight</strong><span class="classifier">array-like of shape (n_samples,), default=None</span></dt><dd><p>Sample weights. If None, then samples are equally weighted.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>self</strong><span class="classifier">object</span></dt><dd><p>Fitted estimator.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="method">
<dt id="sklearn.ensemble.IsolationForest.fit_predict">
<code class="sig-name descname">fit_predict</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">X</em>, <em class="sig-param">y=None</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/base.py#L599"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.ensemble.IsolationForest.fit_predict" title="Permalink to this definition">¶</a></dt>
<dd><p>Perform fit on X and returns labels for X.</p>
<p>Returns -1 for outliers and 1 for inliers.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">ndarray, shape (n_samples, n_features)</span></dt><dd><p>Input data.</p>
</dd>
<dt><strong>y</strong><span class="classifier">Ignored</span></dt><dd><p>Not used, present for API consistency by convention.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>y</strong><span class="classifier">ndarray, shape (n_samples,)</span></dt><dd><p>1 for inliers, -1 for outliers.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="method">
<dt id="sklearn.ensemble.IsolationForest.get_params">
<code class="sig-name descname">get_params</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">deep=True</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/base.py#L173"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.ensemble.IsolationForest.get_params" title="Permalink to this definition">¶</a></dt>
<dd><p>Get parameters for this estimator.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>deep</strong><span class="classifier">bool, default=True</span></dt><dd><p>If True, will return the parameters for this estimator and
contained subobjects that are estimators.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>params</strong><span class="classifier">mapping of string to any</span></dt><dd><p>Parameter names mapped to their values.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="method">
<dt id="sklearn.ensemble.IsolationForest.predict">
<code class="sig-name descname">predict</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">X</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/ensemble/_iforest.py#L309"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.ensemble.IsolationForest.predict" title="Permalink to this definition">¶</a></dt>
<dd><p>Predict if a particular sample is an outlier or not.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">array-like or sparse matrix, shape (n_samples, n_features)</span></dt><dd><p>The input samples. Internally, it will be converted to
<code class="docutils literal notranslate"><span class="pre">dtype=np.float32</span></code> and if a sparse matrix is provided
to a sparse <code class="docutils literal notranslate"><span class="pre">csr_matrix</span></code>.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>is_inlier</strong><span class="classifier">array, shape (n_samples,)</span></dt><dd><p>For each observation, tells whether or not (+1 or -1) it should
be considered as an inlier according to the fitted model.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="method">
<dt id="sklearn.ensemble.IsolationForest.score_samples">
<code class="sig-name descname">score_samples</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">X</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/ensemble/_iforest.py#L364"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.ensemble.IsolationForest.score_samples" title="Permalink to this definition">¶</a></dt>
<dd><p>Opposite of the anomaly score defined in the original paper.</p>
<p>The anomaly score of an input sample is computed as
the mean anomaly score of the trees in the forest.</p>
<p>The measure of normality of an observation given a tree is the depth
of the leaf containing this observation, which is equivalent to
the number of splittings required to isolate this point. In case of
several observations n_left in the leaf, the average path length of
a n_left samples isolation tree is added.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">array-like or sparse matrix, shape (n_samples, n_features)</span></dt><dd><p>The input samples.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>scores</strong><span class="classifier">array, shape (n_samples,)</span></dt><dd><p>The anomaly score of the input samples.
The lower, the more abnormal.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="method">
<dt id="sklearn.ensemble.IsolationForest.set_params">
<code class="sig-name descname">set_params</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">**params</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/base.py#L205"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.ensemble.IsolationForest.set_params" title="Permalink to this definition">¶</a></dt>
<dd><p>Set the parameters of this estimator.</p>
<p>The method works on simple estimators as well as on nested objects
(such as pipelines). The latter have parameters of the form
<code class="docutils literal notranslate"><span class="pre">&lt;component&gt;__&lt;parameter&gt;</span></code> so that it’s possible to update each
component of a nested object.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>**params</strong><span class="classifier">dict</span></dt><dd><p>Estimator parameters.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>self</strong><span class="classifier">object</span></dt><dd><p>Estimator instance.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

</dd></dl>

<div class="section" id="examples-using-sklearn-ensemble-isolationforest">
<h2>Examples using <code class="docutils literal notranslate"><span class="pre">sklearn.ensemble.IsolationForest</span></code><a class="headerlink" href="#examples-using-sklearn-ensemble-isolationforest" title="Permalink to this headline">¶</a></h2>
<div class="sphx-glr-thumbcontainer" tooltip="This example shows characteristics of different anomaly detection algorithms on 2D datasets. Da..."><div class="figure align-default" id="id3">
<img alt="../../_images/sphx_glr_plot_anomaly_comparison_thumb.png" src="../../_images/sphx_glr_plot_anomaly_comparison_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/plot_anomaly_comparison.html#sphx-glr-auto-examples-plot-anomaly-comparison-py"><span class="std std-ref">Comparing anomaly detection algorithms for outlier detection on toy datasets</span></a></span><a class="headerlink" href="#id3" title="Permalink to this image">¶</a></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="An example using sklearn.ensemble.IsolationForest for anomaly detection."><div class="figure align-default" id="id4">
<img alt="../../_images/sphx_glr_plot_isolation_forest_thumb.png" src="../../_images/sphx_glr_plot_isolation_forest_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/ensemble/plot_isolation_forest.html#sphx-glr-auto-examples-ensemble-plot-isolation-forest-py"><span class="std std-ref">IsolationForest example</span></a></span><a class="headerlink" href="#id4" title="Permalink to this image">¶</a></p>
</div>
</div><div class="clearer"></div></div>
</div>


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